Researchers have developed a tandem depolymerization and hydrogenolysis strategy that selectively converts polystyrene waste into toluene with 99 % selectivity and 83.5 wt% yield. The two‑stage process first subjects polystyrene to hydropholysis at 475 °C to generate aromatic intermediates, followed by vapor‑phase hydrogenolysis at 275 °C and 0.4 MPa over a ruthenium single‑atom modified Co₃O₄ catalyst (RuSA/Co₃O₄). The system is compatible with various disposable polystyrene‑based raw materials, delivering approximately 81 wt% yield and >99 % selectivity for toluene. This work represents a transformative paradigm for polystyrene valorization, moving from non‑selective deconstruction toward selective synthesis.

Researchers have developed a tandem depolymerization and hydrogenolysis strategy that selectively converts polystyrene waste into toluene with 99 % selectivity and 83.5 wt% yield. The two‑stage process first subjects polystyrene to hydropholysis at 475 °C to generate aromatic intermediates, followed by vapor‑phase hydrogenolysis at 275 °C and 0.4 MPa over a ruthenium single‑atom modified Co₃O₄ catalyst (RuSA/Co₃O₄). The system is compatible with various disposable polystyrene‑based raw materials, delivering approximately 81 wt% yield and >99 % selectivity for toluene. This work represents a transformative paradigm for polystyrene valorization, moving from non‑selective deconstruction toward selective synthesis.

Artificial intelligence is fundamentally transforming chemical engineering across all sub‑disciplines, from reaction engineering and catalyst design to process control, materials discovery, and plant operations. This review highlights how machine learning and digital twins are enabling real‑time process optimization, predictive safety monitoring, and sustainable manufacturing. Foundation models and autonomous laboratories are accelerating molecular and materials discovery, while large language models extract structured knowledge from scientific literature. The convergence of AI with chemical engineering is driving a paradigm shift toward smarter, greener, and more efficient production systems.

Researchers have developed a novel acidic dyeing strategy using silicon‑containing reactive dyes and a silane coupling agent (KH550). Under weak acidic conditions (pH 5.0), a three‑dimensional “dye–crosslinker–fabric” network is formed through reactions between silanol groups and fabric hydroxyl groups, between the amino groups of KH550 and the reactive groups of the dyes, and via self‑condensation of silanol groups. This method achieves high fixation rates of 91.3 %–96.4 % on cotton with excellent wash and rubbing fastness. By selecting compatible disperse dyes, one‑bath one‑step dyeing of polyester/cotton blends is realized, with total dye utilization exceeding 90 %. The process reduces water, electricity, and thermal energy consumption by approximately 50 %, 49 %, and 37 %, respectively, compared to conventional two‑bath methods.

The Dongguan Institute of Materials Science and Technology, Chinese Academy of Sciences (DIMST, CAS) is one of the newest national research institutes under the Chinese Academy of Sciences. Jointly established by the Chinese Academy of Sciences, the People's Government of Guangdong Province, and the People's Government of Dongguan, the Institute is located in Songshan Lake Science City, the core innovation hub of the Guangdong-Hong Kong-Macao Greater Bay Area. DIMST is committed to becoming a world-leading center for materials science, addressing frontier scientific challenges while accelerating the translation of fundamental discoveries into industrial technologies.

As part of this vision, the New Energy Materials Department is expanding its international research team to tackle some of the most pressing challenges in sustainable energy.